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SparkException:アセンブルする値をnullにすることはできません

StandardScalerを使用して機能を正規化します。

これが私のコードです:

val Array(trainingData, testData) = dataset.randomSplit(Array(0.7,0.3))
val vectorAssembler = new VectorAssembler().setInputCols(inputCols).setOutputCol("features").transform(trainingData)   
val stdscaler = new StandardScaler().setInputCol("features").setOutputCol("scaledFeatures").setWithStd(true).setWithMean(false).fit(vectorAssembler)

StandardScalerを使用しようとすると例外がスローされました

[Stage 151:==>                                                    (9 + 2) / 200]16/12/28 20:13:57 WARN scheduler.TaskSetManager: Lost task 31.0 in stage 151.0 (TID 8922, slave1.hadoop.ml): org.Apache.spark.SparkException: Values to assemble cannot be null.
    at org.Apache.spark.ml.feature.VectorAssembler$$anonfun$assemble$1.apply(VectorAssembler.scala:159)
    at org.Apache.spark.ml.feature.VectorAssembler$$anonfun$assemble$1.apply(VectorAssembler.scala:142)
    at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
    at scala.collection.mutable.WrappedArray.foreach(WrappedArray.scala:35)
    at org.Apache.spark.ml.feature.VectorAssembler$.assemble(VectorAssembler.scala:142)
    at org.Apache.spark.ml.feature.VectorAssembler$$anonfun$3.apply(VectorAssembler.scala:98)
    at org.Apache.spark.ml.feature.VectorAssembler$$anonfun$3.apply(VectorAssembler.scala:97)
    at org.Apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)
    at org.Apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.Java:43)
    at org.Apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:370)
    at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
    at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
    at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
    at scala.collection.Iterator$class.foreach(Iterator.scala:893)
    at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
    at scala.collection.TraversableOnce$class.foldLeft(TraversableOnce.scala:157)
    at scala.collection.AbstractIterator.foldLeft(Iterator.scala:1336)
    at scala.collection.TraversableOnce$class.aggregate(TraversableOnce.scala:214)
    at scala.collection.AbstractIterator.aggregate(Iterator.scala:1336)
    at org.Apache.spark.rdd.RDD$$anonfun$treeAggregate$1$$anonfun$24.apply(RDD.scala:1093)
    at org.Apache.spark.rdd.RDD$$anonfun$treeAggregate$1$$anonfun$24.apply(RDD.scala:1093)
    at org.Apache.spark.rdd.RDD$$anonfun$treeAggregate$1$$anonfun$25.apply(RDD.scala:1094)
    at org.Apache.spark.rdd.RDD$$anonfun$treeAggregate$1$$anonfun$25.apply(RDD.scala:1094)
    at org.Apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:766)
    at org.Apache.spark.rdd.RDD$$anonfun$mapPartitions$1$$anonfun$apply$23.apply(RDD.scala:766)
    at org.Apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.Apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
    at org.Apache.spark.rdd.RDD.iterator(RDD.scala:283)
    at org.Apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
    at org.Apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
    at org.Apache.spark.rdd.RDD.iterator(RDD.scala:283)
    at org.Apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:79)
    at org.Apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:47)
    at org.Apache.spark.scheduler.Task.run(Task.scala:85)
    at org.Apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
    at Java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.Java:1142)
    at Java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.Java:617)
    at Java.lang.Thread.run(Thread.Java:745)

VectorAssemblerに問題はありますか?

VectorAssemblerの数行を確認しましたが、問題はないようです。

vectorAssembler.take(5)
13
April

スパーク> = 2.4

Spark 2.4 VectorAssembler extends HasHandleInvalidです。つまり、skipを実行できるということです。

assembler.setHandleInvalid("skip").transform(df).show
+---+---+---------+
| x1| x2| features|
+---+---+---------+
|3.0|4.0|[3.0,4.0]|
+---+---+---------+

keep(MLアルゴリズムがこれを正しく処理する可能性は低いことに注意してください):

assembler.setHandleInvalid("keep").transform(df).show
+----+----+---------+
|  x1|  x2| features|
+----+----+---------+
| 1.0|null|[1.0,NaN]|
|null| 2.0|[NaN,2.0]|
| 3.0| 4.0|[3.0,4.0]|
+----+----+---------+

またはデフォルトはerrorです。

スパーク<2.4

VectorAssemblerに問題はありません。 Spark Vectornullの値を含めることはできません。

import org.Apache.spark.ml.feature.VectorAssembler

val df = Seq(
  (Some(1.0), None), (None, Some(2.0)), (Some(3.0), Some(4.0))
).toDF("x1", "x2")

val assembler = new VectorAssembler()
  .setInputCols(df.columns).setOutputCol("features")

assembler.transform(df).show(3)
org.Apache.spark.SparkException: Failed to execute user defined function($anonfun$3: (struct<x1:double,x2:double>) => vector)
...
Caused by: org.Apache.spark.SparkException: Values to assemble cannot be null.

NullはMLアルゴリズムでは意味がなく、scala.Doubleを使用して表すことはできません。

あなたはどちらかを落とさなければなりません:

assembler.transform(df.na.drop).show(2)
+---+---+---------+
| x1| x2| features|
+---+---+---------+
|3.0|4.0|[3.0,4.0]|
+---+---+---------+

またはfill/impute(参照 欠落している値を平均で置き換える-Spark Dataframe ):)

// For example with averages
val replacements: Map[String,Any] = Map("x1" -> 2.0, "x2" -> 3.0)
assembler.transform(df.na.fill(replacements)).show(3)
+---+---+---------+
| x1| x2| features|
+---+---+---------+
|1.0|3.0|[1.0,3.0]|
|2.0|2.0|[2.0,2.0]|
|3.0|4.0|[3.0,4.0]|
+---+---+---------+

nulls

24
user6910411